Standard
Harvard
Petrov, N
, Mihaylova, L, Gning, A & Angelova, D 2012,
Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterised Likelihood Function. in KK Sabelfeld & I Dimov (eds),
Monte Carlo Methods and Applications : Proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29 - September 2, 2011, Borovets, Bulgaria. De Gruyter Proceedings in Mathematics, De Gruyter, pp. 171-180. <
http://www.degruyter.com/view/product/184575>
APA
Petrov, N.
, Mihaylova, L., Gning, A., & Angelova, D. (2012).
Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterised Likelihood Function. In K. K. Sabelfeld, & I. Dimov (Eds.),
Monte Carlo Methods and Applications : Proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29 - September 2, 2011, Borovets, Bulgaria (pp. 171-180). (De Gruyter Proceedings in Mathematics). De Gruyter.
http://www.degruyter.com/view/product/184575
Vancouver
Petrov N
, Mihaylova L, Gning A, Angelova D.
Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterised Likelihood Function. In Sabelfeld KK, Dimov I, editors, Monte Carlo Methods and Applications : Proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29 - September 2, 2011, Borovets, Bulgaria. De Gruyter. 2012. p. 171-180. (De Gruyter Proceedings in Mathematics).
Author
Bibtex
@inproceedings{6c59d8985d4b421a853181ecc8458772,
title = "Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterised Likelihood Function",
abstract = "Group objects are characterised with multiple measurements originating from different locations of the targets constituting the group. This paper presents a novel Sequential Monte Carlo approach for tracking groups with a large number of components, applicable to various nonlinear problems. The novelty in this work is in the derivation of the likelihood function for nonlinear measurement functions, with sets of measurements belonging to a bounded spatial region. Simulation results are presented when a group of 50 objects is surrounded by a circular region. Estimation results are given for the group object center and extent.",
keywords = "sequential Monte Carlo methods, measurement uncertainty, nonlinear estimation, group object tracking",
author = "Nikolay Petrov and Lyudmila Mihaylova and Amadou Gning and Donka Angelova",
year = "2012",
month = dec,
language = "English",
series = "De Gruyter Proceedings in Mathematics",
publisher = "De Gruyter",
pages = "171--180",
editor = "Sabelfeld, {Karl K.} and Dimov, {Ivan }",
booktitle = "Monte Carlo Methods and Applications",
}
RIS
TY - GEN
T1 - Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterised Likelihood Function
AU - Petrov, Nikolay
AU - Mihaylova, Lyudmila
AU - Gning, Amadou
AU - Angelova, Donka
PY - 2012/12
Y1 - 2012/12
N2 - Group objects are characterised with multiple measurements originating from
different locations of the targets constituting the group. This paper presents a novel Sequential Monte Carlo approach for tracking groups with a large number of components, applicable to various nonlinear problems. The novelty in this work is in the derivation of the likelihood function for nonlinear measurement functions, with sets of measurements belonging to a bounded spatial region. Simulation results are presented when a group of 50 objects is surrounded by a circular region. Estimation results are given for the group object center and extent.
AB - Group objects are characterised with multiple measurements originating from
different locations of the targets constituting the group. This paper presents a novel Sequential Monte Carlo approach for tracking groups with a large number of components, applicable to various nonlinear problems. The novelty in this work is in the derivation of the likelihood function for nonlinear measurement functions, with sets of measurements belonging to a bounded spatial region. Simulation results are presented when a group of 50 objects is surrounded by a circular region. Estimation results are given for the group object center and extent.
KW - sequential Monte Carlo methods
KW - measurement uncertainty
KW - nonlinear estimation
KW - group object tracking
M3 - Conference contribution/Paper
T3 - De Gruyter Proceedings in Mathematics
SP - 171
EP - 180
BT - Monte Carlo Methods and Applications
A2 - Sabelfeld, Karl K.
A2 - Dimov, Ivan
PB - De Gruyter
ER -